Discussion on AI-Based Digital Upgrade and Application Practice of Laboratory Animal Centers
10.12300/j.issn.1674-5817.2024.181
- VernacularTitle:基于人工智能的实验动物中心信息化升级及应用实践
- Author:
Tingjun WANG
1
;
Hao LUO
2
;
Qi CHEN
1
Author Information
1. The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
2. Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou 310018, China
- Publication Type:Journal Article
- Keywords:
Laboratory animal facility management;
Multimodal large language model;
Intelligent recognition;
Information system
- From:
Laboratory Animal and Comparative Medicine
2025;45(4):473-482
- CountryChina
- Language:Chinese
-
Abstract:
Objective In traditional laboratory animal centers, there are issues such as low efficiency in cage scheduling, insufficient supervision of personnel behavior, and difficulty in upgrading aging equipment. This study aims to upgrade the information system of existing laboratory animal centers by applying multimodal large language model technology. This upgrade intends to achieve real-time perception of the status of animal cages, intelligent supervision of experimental personnel behavior, and automated processing of business workflows, thereby improving management efficiency and precision. Methods An AI-based approach for upgrading laboratory animal center informatization was proposed by the First Affiliated Hospital of Zhejiang University School of Medicine,compatible with different breeding equipments. The system architecture, from the bottom up, consisted of three layers: hardware layer, core algorithm layer, and application layer. The hardware layer was equipped with cameras and high-speed network transmission devices for collecting information on cages and personnel. The core algorithm layer utilized multi-stage image preprocessing technology and multimodal large language model recognition technology to extract and identify image information. The application layer integrated the recognition results with the existing information of the animal center to generate real-time cage occupancy heatmaps, which visually and clearly showed the density distribution of cage usage in the laboratory animal center. Results The AI-based management system achieved a cage recognition accuracy of 98.5% and a correct wearing identification rate of laboratory coats of 98.8%. The average image processing time was 3.7 seconds per image, the effective utilization rate of cages increased by 23%, and the turnover efficiency improved by 35%. In addition, the management system could track and warn against non-compliant behaviors in real time. After intelligent recognition, the system detected more violations, with the violation detection rate increasing by 90.6%. After continuous use for three months, the weekly average number of violations decreased by 54.0% compared to the baseline period. Conclusion This study applies multimodal large language model to the field of laboratory animal management, achieving real-time monitoring and automated management of cage identification, thereby improving management efficiency and precision. The system integrates multi-source data such as visual recognition and behavior analysis, establishing a comprehensive intelligent supervision system for experimental personnel. It provides research institutions with efficient, accurate, and cost-effective management tools, promoting the intelligent development of laboratory animal management.
